Voice data designed around real AI workflows.
Sonexis builds human data for training, fine-tuning, evaluation, and benchmarking across voice and conversational AI. Each dataset is scoped to the model, deployment environment, and conversational failure modes the buyer defines.
ASR training and evaluation
The problem
ASR systems often fail with accents, noisy environments, code switching, and informal speech. Data that is too clean overestimates real-world performance.
Sonexis provides
Realistic speech and conversation data with language tags, transcripts where required, speaker structure, and QA notes. Designed around your accent and scenario requirements.
TTS evaluation
The problem
TTS systems need evaluation against natural language variation, pronunciation, and regional speech expectations that synthetic baselines do not capture.
Sonexis provides
Human voice samples, evaluation prompts, and language-specific testing data across Indian and multilingual markets.
Voice agent testing
The problem
Voice agents can perform well in demos and still fail when users interrupt, change intent, reply briefly, or give unclear instructions.
Sonexis provides
Scenario-based conversations with interruptions, corrections, short replies, ambiguous intent, and context shifts. Designed around your deployment scenario.
Conversational AI evaluation
The problem
Conversational systems need to handle messy dialogue, ambiguity, incomplete information, and changing context. These conditions are hard to simulate synthetically.
Sonexis provides
Multi-turn conversations designed around real user behaviour, including topic shifts, partial information, and correction loops.
Multilingual benchmark datasets
The problem
Benchmarks often miss code switching, regional accent variation, and local speech behaviour that determine real-world model quality.
Sonexis provides
Evaluation datasets across Indian and multilingual language combinations, structured to support standardised benchmarking.
Customer support conversations
The problem
Support conversations are messy, emotional, and full of incomplete information. Models that only see clean data will underperform in live support environments.
Sonexis provides
Scenario-driven support dialogues across languages and speaker profiles, designed around common support flows and escalation patterns.
Onboarding and KYC-style conversations
The problem
Real onboarding flows include corrections, hesitations, spelling out information, repetition, and clarification requests. These patterns rarely appear in standard training sets.
Sonexis provides
Structured voice data for onboarding, KYC, and verification flows with natural hesitation patterns and correction behaviour.
Sales and product discovery
The problem
Users compare, explore, ask unclear questions, shift intent, and challenge responses in ways that differ from scripted demos.
Sonexis provides
Natural product discovery conversations and sales-style interactions across languages and buyer personas.
Use case datasets can include structured metadata, QA-reviewed submissions, consent-linked records, and delivery in agreed formats, depending on the agreed scope. Scope is confirmed with the buyer before collection begins.
Discuss your use case.
Tell us what you are building and what data would make the most impact. We will scope the right collection approach.
Scope a Dataset